Revenue Leak Investigation
An autonomous AI agent fleet diagnosed EUR 2.1M in lost revenue from a payment gateway failure — zero SQL queries written. Step through the full investigation below.
€0.0M
Revenue Impact
lost over 47 undetected days
< 0 min
Detection Time
automated agent investigation
0
SQL Queries Written
fully agent-driven analysis
0
Agent Delegations
LangGraph multi-agent fleet
Watch the autonomous agent fleet diagnose the revenue leak in real-time. Click Launch Investigation to begin, or use the phase tabs to jump to any step.
4 autonomous agents · 33 log entries · < 15 min
Click each agent to see its role in the investigation and key finding.
Interactive chart showing checkout-to-purchase conversion over 106 days. Toggle between mobile and desktop to see how the anomaly only affected mobile traffic.
Drag the slider to see how earlier detection would have reduced losses. At €44,681/day, every day matters.
Actual Loss
€2.1M
Revenue Saved
€0
At €44,681/day, every day of delayed detection costs the equivalent of a senior engineer's annual salary.
Click each finding to expand the evidence chain and remediation details.
Click each layer to explore the Bronze → Silver → Gold pipeline that powers the investigation.
Apache Spark 3.5
Distributed data processing engine
Delta Lake
ACID transactions on the data lake
MinIO
S3-compatible object storage
LangGraph
Agent state machine orchestration
FastAPI
Agent API gateway & endpoints
Qwen 2.5
Local LLM for agent reasoning
Airflow
Pipeline scheduling & monitoring
PostgreSQL
Metadata & session storage
Docker
Container orchestration
Prometheus
Metrics collection & alerting
This investigation demonstrates how autonomous AI agents eliminate the hypothesis-query-iterate cycle — from anomaly detection through multi-agent root cause analysis to automated remediation deployment.
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